Grayscale-based camera perception

ABSTRACT

A method, apparatus, and system for identifying objects based on grayscale images in the operation of an autonomous driving vehicle is disclosed. In one embodiment, one or more first color images are received from a camera mounted at an autonomous driving vehicle (ADV). One or more first grayscale images are generated based on the one or more first color images, which comprises converting each of the one or more first color images into one of the first grayscale images. One or more objects in the one or more first grayscale images are identified based on a pre-trained grayscale perception model. A trajectory for the ADV is planned based at least in part on the identified one or more objects. Control signals are generated to drive the ADV based on the planned trajectory.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous driving vehicles. More particularly, embodiments of thedisclosure relate to identifying objects in the operation of autonomousdriving vehicles.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

Object identification based on images captured by onboard cameras issometimes utilized as part of the perception process in the operation ofautonomous driving vehicles. Color images usually contain moreinformation than grayscale images. However, for cameras installed insidethe vehicle, the colors in the captured color images can be distorteddue to the tinted vehicle window shield or anti-fog or anti-frostvehicle window coating. The color distortion in the captured colorimages may negatively impact the object identification process that isbased on these captured color images.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousdriving vehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of an autonomousdriving system used with an autonomous driving vehicle according to oneembodiment.

FIG. 4 is a block diagram illustrating various modules utilized in theidentification of objects based on grayscale images in the operation ofan autonomous driving vehicle according to one embodiment.

FIG. 5 is a block diagram illustrating various modules utilized in thetraining of the grayscale perception model according to one embodiment.

FIG. 6 is a diagram illustrating an example second color image and anexample second grayscale image according to one embodiment.

FIG. 7 is a flowchart illustrating an example method for identifyingobjects based on grayscale images in the operation of an autonomousdriving vehicle according to one embodiment.

FIG. 8 is a flowchart illustrating an example method for training thegrayscale perception model according to one embodiment.

FIG. 9 is a flowchart illustrating an example method for generating andutilizing a grayscale perception model according to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

A method, apparatus, and system for identifying objects based ongrayscale images in the operation of an autonomous driving vehicle isdisclosed. According to some embodiments, one or more first color imagesare received from a camera mounted at an autonomous driving vehicle(ADV). One or more first grayscale images are generated based on the oneor more first color images, which comprises converting each of the oneor more first color images into one of the first grayscale images. Oneor more objects in the one or more first grayscale images are identifiedbased on a pre-trained grayscale perception model. A trajectory for theADV is planned based at least in part on the identified one or moreobjects. Control signals are generated to drive the ADV based on theplanned trajectory.

In one embodiment, the pre-trained grayscale perception model has beentrained based on a plurality of second color images that are pre-labeledwith object labels. In particular, each object label associates a regionof an image with an object classification. In one embodiment, theplurality of second color images have been pre-labeled with objectlabels either manually or automatically based on a model.

In one embodiment, to train the grayscale perception model, theplurality of second color images that are pre-labeled with object labelsare received. A plurality of second grayscale images are generated basedon the plurality of second color images, which comprises converting eachof the plurality of second color images into one of the plurality ofsecond grayscale images. The plurality of second grayscale images arelabeled with object labels, which comprises duplicating the objectlabels associated with the plurality of second color images onto theplurality of second grayscale images respectively. The grayscaleperception model is generated based on the plurality of second grayscaleimages labeled with the object labels, which comprises utilizing theplurality of labeled second grayscale images as training data.

In one embodiment, duplicating the object labels associated with theplurality of second color images onto the plurality of second grayscaleimages respectively comprises associating each of the object labels onthe plurality of second color images with a corresponding one of theplurality of second grayscale images while preserving the image regionand object classification information of the object label.

In one embodiment, a same conversion technique is utilized in theconversion of each of the plurality of second color images into one ofthe plurality of second grayscale images and in the conversion of eachof the one or more first color images into one of the first grayscaleimages. In one embodiment, the one or more first color images are in acolor space based on a red, green, and blue (RGB) color model. In oneembodiment, converting each of the one or more first color images intoone of the first grayscale images comprises performing a perceptualluminance-preserving conversion.

FIG. 1 is a block diagram illustrating an autonomous driving networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1 , network configuration 100 includes autonomous drivingvehicle (ADV) 101 that may be communicatively coupled to one or moreservers 103-104 over a network 102. Although there is one ADV shown,multiple ADVs can be coupled to each other and/or coupled to servers103-104 over network 102. Network 102 may be any type of networks suchas a local area network (LAN), a wide area network (WAN) such as theInternet, a cellular network, a satellite network, or a combinationthereof, wired or wireless. Server(s) 103-104 may be any kind of serversor a cluster of servers, such as Web or cloud servers, applicationservers, backend servers, or a combination thereof. Servers 103-104 maybe data analytics servers, content servers, traffic information servers,map and point of interest (MPOI) servers, or location servers, etc.

An ADV refers to a vehicle that can be configured to in an autonomousmode in which the vehicle navigates through an environment with littleor no input from a driver. Such an ADV can include a sensor systemhaving one or more sensors that are configured to detect informationabout the environment in which the vehicle operates. The vehicle and itsassociated controller(s) use the detected information to navigatethrough the environment. ADV 101 can operate in a manual mode, a fullautonomous mode, or a partial autonomous mode.

In one embodiment, ADV 101 includes, but is not limited to, autonomousdriving system (ADS) 110, vehicle control system 111, wirelesscommunication system 112, user interface system 113, and sensor system115. ADV 101 may further include certain common components included inordinary vehicles, such as, an engine, wheels, steering wheel,transmission, etc., which may be controlled by vehicle control system111 and/or ADS 110 using a variety of communication signals and/orcommands, such as, for example, acceleration signals or commands,deceleration signals or commands, steering signals or commands, brakingsignals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2 , in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the ADV. IMU unit 213 may sense position and orientationchanges of the ADV based on inertial acceleration. Radar unit 214 mayrepresent a system that utilizes radio signals to sense objects withinthe local environment of the ADV. In some embodiments, in addition tosensing objects, radar unit 214 may additionally sense the speed and/orheading of the objects. LIDAR unit 215 may sense objects in theenvironment in which the ADV is located using lasers. LIDAR unit 215could include one or more laser sources, a laser scanner, and one ormore detectors, among other system components. Cameras 211 may includeone or more devices to capture images of the environment surrounding theADV. Cameras 211 may be still cameras and/or video cameras. A camera maybe mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theADV. A steering sensor may be configured to sense the steering angle ofa steering wheel, wheels of the vehicle, or a combination thereof. Athrottle sensor and a braking sensor sense the throttle position andbraking position of the vehicle, respectively. In some situations, athrottle sensor and a braking sensor may be integrated as an integratedthrottle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn controls the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1 , wireless communication system 112 is to allowcommunication between ADV 101 and external systems, such as devices,sensors, other vehicles, etc. For example, wireless communication system112 can wirelessly communicate with one or more devices directly or viaa communication network, such as servers 103-104 over network 102.Wireless communication system 112 can use any cellular communicationnetwork or a wireless local area network (WLAN), e.g., using WiFi tocommunicate with another component or system. Wireless communicationsystem 112 could communicate directly with a device (e.g., a mobiledevice of a passenger, a display device, a speaker within vehicle 101),for example, using an infrared link, Bluetooth, etc. User interfacesystem 113 may be part of peripheral devices implemented within vehicle101 including, for example, a keyboard, a touch screen display device, amicrophone, and a speaker, etc.

Some or all of the functions of ADV 101 may be controlled or managed byADS 110, especially when operating in an autonomous driving mode. ADS110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, ADS 110 may beintegrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. ADS 110obtains the trip related data. For example, ADS 110 may obtain locationand route data from an MPOI server, which may be a part of servers103-104. The location server provides location services and the MPOIserver provides map services and the POIs of certain locations.Alternatively, such location and MPOI information may be cached locallyin a persistent storage device of ADS 110.

While ADV 101 is moving along the route, ADS 110 may also obtainreal-time traffic information from a traffic information system orserver (TIS). Note that servers 103-104 may be operated by a third partyentity. Alternatively, the functionalities of servers 103-104 may beintegrated with ADS 110. Based on the real-time traffic information,MPOI information, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), ADS 110 can plan an optimal routeand drive vehicle 101, for example, via control system 111, according tothe planned route to reach the specified destination safely andefficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either ADVs or regular vehicles driven by human drivers.Driving statistics 123 include information indicating the drivingcommands (e.g., throttle, brake, steering commands) issued and responsesof the vehicles (e.g., speeds, accelerations, decelerations, directions)captured by sensors of the vehicles at different points in time. Drivingstatistics 123 may further include information describing the drivingenvironments at different points in time, such as, for example, routes(including starting and destination locations), MPOIs, road conditions,weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. In one embodiment, algorithms 124 may include atrained model that can be utilized for identifying objects in grayscaleimages. Algorithms 124 can then be uploaded on ADVs to be utilizedduring autonomous driving in real-time.

FIGS. 3A and 3B are block diagrams illustrating an example of anautonomous driving system used with an ADV according to one embodiment.System 300 may be implemented as a part of ADV 101 of FIG. 1 including,but is not limited to, ADS 110, control system 111, and sensor system115. Referring to FIGS. 3A-3B, ADS 110 includes, but is not limited to,localization module 301, perception module 302, prediction module 303,decision module 304, planning module 305, control module 306, routingmodule 307, grayscale image conversion module 308.

Some or all of modules 301-308 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2 . Some of modules301-308 may be integrated together as an integrated module.

Localization module 301 determines a current location of ADV 300 (e.g.,leveraging GPS unit 212) and manages any data related to a trip or routeof a user. Localization module 301 (also referred to as a map and routemodule) manages any data related to a trip or route of a user. A usermay log in and specify a starting location and a destination of a trip,for example, via a user interface. Localization module 301 communicateswith other components of ADV 300, such as map and route data 311, toobtain the trip related data. For example, localization module 301 mayobtain location and route data from a location server and a map and POI(MPOI) server. A location server provides location services and an MPOIserver provides map services and the POIs of certain locations, whichmay be cached as part of map and route data 311. While ADV 300 is movingalong the route, localization module 301 may also obtain real-timetraffic information from a traffic information system or server.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration, traffic light signals, arelative position of another vehicle, a pedestrian, a building,crosswalk, or other traffic related signs (e.g., stop signs, yieldsigns), etc., for example, in a form of an object. The laneconfiguration includes information describing a lane or lanes, such as,for example, a shape of the lane (e.g., straight or curvature), a widthof the lane, how many lanes in a road, one-way or two-way lane, mergingor splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of the ADV. The objects can includetraffic signals, road way boundaries, other vehicles, pedestrians,and/or obstacles, etc. The computer vision system may use an objectrecognition algorithm, video tracking, and other computer visiontechniques. In some embodiments, the computer vision system can map anenvironment, track objects, and estimate the speed of objects, etc.Perception module 302 can also detect objects based on other sensorsdata provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal routes in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the ADV, as well as driving parameters(e.g., distance, speed, and/or turning angle), using a reference lineprovided by routing module 307 as a basis. That is, for a given object,decision module 304 decides what to do with the object, while planningmodule 305 determines how to do it. For example, for a given object,decision module 304 may decide to pass the object, while planning module305 may determine whether to pass on the left side or right side of theobject. Planning and control data is generated by planning module 305including information describing how vehicle 300 would move in a nextmoving cycle (e.g., next route/path segment). For example, the planningand control data may instruct vehicle 300 to move 10 meters at a speedof 30 miles per hour (mph), then change to a right lane at the speed of25 mph.

Based on the planning and control data, control module 306 controls anddrives the ADV, by sending proper commands or signals to vehicle controlsystem 111, according to a route or path defined by the planning andcontrol data. The planning and control data include sufficientinformation to drive the vehicle from a first point to a second point ofa route or path using appropriate vehicle settings or driving parameters(e.g., throttle, braking, steering commands) at different points in timealong the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as driving cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or driving cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the ADV. For example, the navigation systemmay determine a series of speeds and directional headings to affectmovement of the ADV along a path that substantially avoids perceivedobstacles while generally advancing the ADV along a roadway-based pathleading to an ultimate destination. The destination may be set accordingto user inputs via user interface system 113. The navigation system mayupdate the driving path dynamically while the ADV is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the ADV.

Referring to FIG. 4 , a block diagram 400 illustrating various modulesutilized in the identification of objects based on grayscale images inthe operation of an autonomous driving vehicle according to oneembodiment is shown. One or more first color images 402 are received atperception module 302 from a camera 211 mounted at an autonomous drivingvehicle (ADV) 101. Within the perception module 302, one or more firstgrayscale images 404 are generated at the grayscale image conversionmodule 308 based on the one or more first color images 402. Thegeneration of first grayscale images 404 comprises converting each ofthe one or more first color images 402 into one of the first grayscaleimages 404 at the grayscale image conversion module 308. The conversionof color images into grayscale images is well known in the art.Well-known techniques, such as a perceptual luminance-preservingconversion may be utilized to convert color images into grayscaleimages. One or more objects 408 in the one or more first grayscaleimages 404 are identified at the perception module 302 based on apre-trained grayscale perception model 406. A trajectory for the ADV 101is planned at the planning module 305 based at least in part on theidentified one or more objects 408. Control signals are generated at thecontrol module 306 to drive the ADV 101 based on the planned trajectory.

In one embodiment, the pre-trained grayscale perception model has beentrained based on a plurality of second color images that are pre-labeledwith object labels. In particular, each object label associates a regionof an image with an object classification. In one embodiment, theplurality of second color images have been pre-labeled with objectlabels either manually or automatically based on a model.

Referring to FIG. 5 , a block diagram 500 illustrating various modulesutilized in the training of the grayscale perception model according toone embodiment is shown. The plurality of second color images 502 thatare pre-labeled with object labels are received at the grayscale imageconversion module 504. The grayscale image conversion module 504 may beexecuted at server 103 and is similar to the grayscale image conversionmodule 308. A plurality of second grayscale images 506 are generated bythe grayscale image conversion module 504 based on the plurality ofsecond color images 502. The generation of the plurality of secondgrayscale images 506 comprises converting each of the plurality ofsecond color images 502 into one of the plurality of second grayscaleimages 506. The conversion of color images into grayscale images is wellknown in the art. Well-known techniques, such as a perceptualluminance-preserving conversion may be utilized to convert color imagesinto grayscale images. Within the machine learning engine 122, theplurality of second grayscale images 506 are labeled with object labelsat the object label duplication module 508. Labeling the plurality ofsecond grayscale images 506 comprises duplicating the object labelsassociated with the plurality of second color images 502 onto theplurality of second grayscale images 506 respectively. In particular,the object label duplication module 508 associates each of the objectlabels on the plurality of second color images 502 with a correspondingone of the plurality of second grayscale images 506 while preserving theimage region and object classification information of the object label.The grayscale perception model 406 is generated at the machine learningengine 122 based on the plurality of second grayscale images 506 labeledwith the object labels. The generation of the grayscale perception model406 comprises utilizing the plurality of labeled second grayscale images506 as training data.

In one embodiment, a same conversion technique is utilized in theconversion of each of the plurality of second color images into one ofthe plurality of second grayscale images and in the conversion of eachof the one or more first color images into one of the first grayscaleimages. For example, the conversion technique may comprise a perceptualluminance-preserving conversion.

Referring to FIG. 6 , a diagram 600 illustrating an example second colorimage and an example second grayscale image according to one embodimentis shown. It should be appreciated that it is only due to technicallimitations associated with patent applications that the color image 602appears to be in grayscale in the present disclosure. The second colorimage 602 has been pre-labeled with 3 object labels 604. Each objectlabel 604 associates a region of the second color image 602 with anobject classification. In this example, all object labels 604 include a“vehicle” classification. Other possible object classifications mayinclude a pedestrian, a bicycle, a plant, a traffic light, etc. Theobject labels 604 may have been generated either manually orautomatically based on a model. Therefore, the grayscale imageconversion module 504 may convert the second color image 602 into asecond grayscale image 606. Then, the object label duplication module508 may duplicate the object labels 604 on the second color image 602onto the second grayscale image 606. The duplicated object labels 608preserve the image region and object classification information of theobject labels 604. In other words, the object labels 608 associate thecorresponding regions in the second grayscale image 606 with the same“vehicle” classification. Thereafter, the second grayscale image 606 andthe associated object labels 608, together with other similar secondgrayscale images and associated object labels, may be utilized astraining data in the generation of the grayscale perception model 406 atthe machine learning engine 122.

Referring to FIG. 7 , a flowchart illustrating an example method 700 foridentifying objects based on grayscale images in the operation of anautonomous driving vehicle according to one embodiment is shown. Theprocess 700 may be implemented with appropriate hardware, software, or acombination thereof. At block 710, one or more first color images arereceived from a camera mounted at an autonomous driving vehicle (ADV).At block 720, one or more first grayscale images are generated based onthe one or more first color images. The generation of first grayscaleimages comprises converting each of the one or more first color imagesinto one of the first grayscale images. The conversion of color imagesinto grayscale images is well known in the art. Well-known techniques,such as a perceptual luminance-preserving conversion may be utilized toconvert color images into grayscale images. At block 730, one or moreobjects in the one or more first grayscale images are identified basedon a pre-trained grayscale perception model. At block 740, a trajectoryfor the ADV is planned based at least in part on the identified one ormore objects. At block 750, control signals are generated to drive theADV based on the planned trajectory.

Referring to FIG. 8 , a flowchart illustrating an example method 800 fortraining the grayscale perception model according to one embodiment isshown. The process 800 may be implemented with appropriate hardware,software, or a combination thereof. At block 810, the plurality ofsecond color images that are pre-labeled with object labels arereceived. At block 820, a plurality of second grayscale images aregenerated based on the plurality of second color images. The generationof the plurality of second grayscale images comprises converting each ofthe plurality of second color images into one of the plurality of secondgrayscale images. The conversion of color images into grayscale imagesis well known in the art. Well-known techniques, such as a perceptualluminance-preserving conversion may be utilized to convert color imagesinto grayscale images. At block 830, the plurality of second grayscaleimages are labeled with object labels, which comprises duplicating theobject labels associated with the plurality of second color images ontothe plurality of second grayscale images respectively. In particular,each of the object labels on the plurality of second color images isassociated with a corresponding one of the plurality of second grayscaleimages while the image region and object classification information ofthe object label is preserved. At block 840, the grayscale perceptionmodel is generated based on the plurality of second grayscale imageslabeled with the object labels. The generation of the grayscaleperception model comprises utilizing the plurality of labeled secondgrayscale images as training data.

In one embodiment, a same conversion technique is utilized in theconversion of each of the plurality of second color images into one ofthe plurality of second grayscale images and in the conversion of eachof the one or more first color images into one of the first grayscaleimages. For example, the conversion technique may comprise a perceptualluminance-preserving conversion.

Referring to FIG. 9 , a flowchart illustrating an example method 900 forgenerating and utilizing a grayscale perception model according to oneembodiment is shown. The process 900 may be implemented with appropriatehardware, software, or a combination thereof. At block 910, a grayscaleperception model may be generated. The grayscale perception model may begenerated based on well-known machine learning techniques. The trainingdata utilized in the generation of the grayscale perception model maycomprise second grayscale images that are labeled with object labels.The labeled second grayscale images may be derived from second colorimages that are pre-labeled with object labels. At block 920, thegrayscale perception model may be utilized to identify objects in theoperation of an autonomous driving vehicle. Objects may be identified inthe first grayscale images based on the grayscale perception model. Thefirst grayscale images may be derived from live first color images thatare captured by a camera installed on the autonomous driving vehicle.

Therefore, with embodiments described herein, grayscale images ratherthan color images are directly utilized in the object identificationprocess in the operation of an autonomous driving vehicle. This helpsimprove the object identification process by reducing or eliminating thepotential negative impact to the object identification process caused bydistorted colors in the live color images captured by the onboardcamera. The color distortion may arise when, for example, the camera isinstalled inside the vehicle, and there are tinted vehicle windowshields or coating. As a result, and in particular, traffic lightdetection, plant detection, and pedestrian detection, etc., whichotherwise would have at least partially relied on certain presumptionsabout color, can be improved. The embodiments may also be helpful duringdark or cloudy weather conditions as the color information in thecaptured color images is of lower quality under such conditions.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method for operating anautonomous driving vehicle, the method comprising: receiving one or morefirst color images from a camera mounted at an autonomous drivingvehicle (ADV); generating one or more first grayscale images based onthe one or more first color images, including converting each of the oneor more first color images into one of the first grayscale images;receiving a plurality of second color images that are pre-labeled withobject labels; generating a pre-trained grayscale perception model basedon a plurality of second grayscale images labeled with object labelsduplicated from the plurality of second color images; identifying one ormore objects in the one or more first grayscale images using thepre-trained grayscale perception model; planning a trajectory for theADV based at least in part on the identified one or more objects; andgenerating control signals to drive the ADV based on the plannedtrajectory.
 2. The method of claim 1, wherein each object labelassociates a region of an image with an object classification.
 3. Themethod of claim 2, wherein the plurality of second color images havebeen pre-labeled with object labels either manually or automaticallybased on a model.
 4. The method of claim 2, wherein generating thepre-trained grayscale perception model comprises: generating theplurality of second grayscale images based on the plurality of secondcolor images, including converting each of the plurality of second colorimages into one of the plurality of second grayscale images; labelingthe plurality of second grayscale images with object labels, includingduplicating the object labels associated with the plurality of secondcolor images onto the plurality of second grayscale images respectively;and generating the pre-trained grayscale perception model based on theplurality of second grayscale images labeled with the object labels,including utilizing the plurality of labeled second grayscale images astraining data.
 5. The method of claim 4, wherein duplicating the objectlabels associated with the plurality of second color images onto theplurality of second grayscale images respectively comprises associatingeach of the object labels on the plurality of second color images with acorresponding one of the plurality of second grayscale images whilepreserving the region of the image and object classification informationof the object label.
 6. The method of claim 4, wherein a same conversiontechnique is utilized in the conversion of each of the plurality ofsecond color images into one of the plurality of second grayscale imagesand in the conversion of each of the one or more first color images intoone of the first grayscale images.
 7. The method of claim 1, wherein theone or more first color images are in a color space based on a red,green, and blue (RGB) color model.
 8. The method of claim 1, whereinconverting each of the one or more first color images into one of thefirst grayscale images comprises performing a perceptualluminance-preserving conversion.
 9. A non-transitory machine-readablemedium having instructions stored therein, which when executed by aprocessor, cause the processor to perform operations, the operationscomprising: receiving one or more first color images from a cameramounted at an autonomous driving vehicle (ADV); generating one or morefirst grayscale images based on the one or more first color images,including converting each of the one or more first color images into oneof the first grayscale images; receiving a plurality of second colorimages that are pre-labeled with object labels; generating a pre-trainedgrayscale perception model based on a plurality of second grayscaleimages labeled with object labels duplicated from the plurality ofsecond color images; identifying one or more objects in the one or morefirst grayscale images using the pre-trained grayscale perception model;planning a trajectory for the ADV based at least in part on theidentified one or more objects; and generating control signals to drivethe ADV based on the planned trajectory.
 10. The non-transitorymachine-readable medium of claim 9, wherein each object label associatesa region of an image with an object classification.
 11. Thenon-transitory machine-readable medium of claim 10, wherein theplurality of second color images have been pre-labeled with objectlabels either manually or automatically based on a model.
 12. Thenon-transitory machine-readable medium of claim 10, wherein generatingthe pre-trained grayscale perception model comprises: generating theplurality of second grayscale images based on the plurality of secondcolor images, including converting each of the plurality of second colorimages into one of the plurality of second grayscale images; labelingthe plurality of second grayscale images with object labels, includingduplicating the object labels associated with the plurality of secondcolor images onto the plurality of second grayscale images respectively;and generating the pre-trained grayscale perception model based on theplurality of second grayscale images labeled with the object labels,including utilizing the plurality of labeled second grayscale images astraining data.
 13. The non-transitory machine-readable medium of claim12, wherein duplicating the object labels associated with the pluralityof second color images onto the plurality of second grayscale imagesrespectively comprises associating each of the object labels on theplurality of second color images with a corresponding one of theplurality of second grayscale images while preserving the region of theimage and object classification information of the object label.
 14. Thenon-transitory machine-readable medium of claim 12, wherein a sameconversion technique is utilized in the conversion of each of theplurality of second color images into one of the plurality of secondgrayscale images and in the conversion of each of the one or more firstcolor images into one of the first grayscale images.
 15. Thenon-transitory machine-readable medium of claim 9, wherein the one ormore first color images are in a color space based on a red, green, andblue (RGB) color model.
 16. The non-transitory machine-readable mediumof claim 9, wherein converting each of the one or more first colorimages into one of the first grayscale images comprises performing aperceptual luminance-preserving conversion.
 17. A data processingsystem, comprising: a processor; and a memory coupled to the processorto store instructions, which when executed by the processor, cause theprocessor to perform operations, the operations including receiving oneor more first color images from a camera mounted at an autonomousdriving vehicle (ADV), generating one or more first grayscale imagesbased on the one or more first color images, including converting eachof the one or more first color images into one of the first grayscaleimages, receiving a plurality of second color images that arepre-labeled with object labels, generating a pre-trained grayscaleperception model based on a plurality of second grayscale images labeledwith object labels duplicated from the plurality of second color images,identifying one or more objects in the one or more first grayscaleimages using the pre-trained grayscale perception model, planning atrajectory for the ADV based at least in part on the identified one ormore objects, and generating control signals to drive the ADV based onthe planned trajectory.
 18. The data processing system of claim 17,wherein each object label associates a region of an image with an objectclassification.
 19. The data processing system of claim 18, wherein theplurality of second color images have been pre-labeled with objectlabels either manually or automatically based on a model.
 20. The dataprocessing system of claim 18, wherein generating the pre-trainedgrayscale perception model comprises: generating the plurality of secondgrayscale images based on the plurality of second color images,including converting each of the plurality of second color images intoone of the plurality of second grayscale images; labeling the pluralityof second grayscale images with object labels, including duplicating theobject labels associated with the plurality of second color images ontothe plurality of second grayscale images respectively; and generatingthe pre-trained grayscale perception model based on the plurality ofsecond grayscale images labeled with the object labels, includingutilizing the plurality of labeled second grayscale images as trainingdata.